Lung cancer remains the leading cause of mortality cancer. In 1999, there were approximately 170,000 new cases of lung cancer in the U.S., where approximately one in every eighteen women and approximately one in every twelve men develop lung cancer. Lung tumors (visible on chest film as pulmonary nodules) are the most common manifestation of lung cancer being the principal cause of cancer-related deaths. A pulmonary nodule is an approximately spherical volume of higher density tissue being visible in an X-ray lung image, and early detection of pulmonary nodules may increase the patient's chance of survival. Detecting pulmonary nodules, however, is a complicated task. Nodules typically show up in an X-ray lung image as relatively low-contrast white circular objects within the lung fields. The difficulty for computer aided image data search schemes is distinguishing true nodules from (overlapping) shadows, vessels and ribs.
The early stage detection of lung cancer therefore remains an important goal in medical research. Regular chest radiography and sputum examination programs have proven ineffective in reducing mortality rates. Although screening for lung cancer with chest X-rays can detect early lung cancer, such screening can also possibly produce many false-positive test results, causing needless extra tests. Furthermore, while large (e.g., greater than 1 cm in diameter) malignant nodules are often relatively easy to detect with conventional screening equipment and can be diagnosed with needle biopsy or bronchoscopy techniques, these techniques are typically unsuitable for detecting smaller nodules, particularly if such nodules are located deep in the lung tissue or away from large airways. In cases involving small nodules, conventional methods rely on analyzing the growth rate of the nodule over time. As may be imagined, waiting a period of time to determine whether a lung nodule is malignant provides a possibly malignant nodule time to grow and become a larger health risk. Hence, while it is preferable to detect lung cancer as early as possible, many conventional methods for detecting lung cancer require waiting periods. Thus, many of these techniques have been found to be unsuitable for early stage lung cancer detection.
At present, low-dose spiral computed tomography (LDCT) is of prime interest for screening (high risk) groups for early detection of lung cancer and is being studied by various groups, including the National Cancer Institute. LDCT provides chest scans with very high spatial, temporal, and contrast resolution of anatomic structures and is able to gather a complete 3D volume of a human thorax in a single breath-hold. Hence, for these reasons, in recent years most lung cancer screening programs are being investigated in the United States and Japan with LDCT as the screening modality of choice.
Automatic screening of image data from LDCT typically involves selecting initial candidate lung abnormalities (pulmonary nodules). Next, the false candidates, called false positive nodules (FPNs), are partially eliminated while preserving the true positive nodules (TPNs).
When selecting initial candidates, conformal nodule filtering or unsharp masking can enhance nodules and suppress other structures to separate the candidates from the background by simple thresholding or multiple gray-level thresholding techniques. A series of 3D cylindrical and spherical filters may be used to detect small lung nodules from high resolution CT images. Circular and semicircular nodule candidates may be detected by template matching. However, these spherical, cylindrical, or circular assumptions are typically not adequate for describing the general geometry of the lesions. This is because their shape can be irregular due to the speculation or the attachments to the pleural surface (i.e., juxtapleural and peripheral) and vessels (i.e., vascularized). Morphological operators may be used to detect lung nodules. The drawbacks to these approaches are the difficulties in detecting lung wall nodules. Also, there are other pattern recognition techniques used in detection of lung nodules such as clustering, linear discriminant functions, rule-based classification, Hough transforms, connected component analysis of thresholded CT slices, gray level distance transforms, and patient-specific a priori models.
FPNs may be excluded by feature extraction and classification. Such features as circularity, size, contrast, or local curvature that are extracted by morphological techniques, or artificial neural networks (ANN), may be used as post-classifiers. Also, there are a number of classification techniques used in the final stage of some nodule detection systems to reduce the FPNs such as: rule-based or linear classifiers; template matching; nearest cluster; and Markov random field.
One of the most popular and promising directions of detecting small cancerous nodules is to analyze their changes over time. For example, attempts have been made to classify nodules as benign or malignant by estimating their growth rate. Tracking temporal nodule behavior is a challenging task, however, because of changes in the patient's position at each data acquisition, as well as effects of heart beats and respiration. In order to accurately measure how the nodules are developing in time, all of these motions need to be compensated by registering the LDCT data sets taken at different times. Several methods have been proposed for solving medical image registration problems and excluding the lung motions; however, the accuracy of nodule classification based on the growth rate still remains below clinical requirements.
Therefore, a need continues to exist in the art for improved image processing techniques for use in diagnosing malignant pulmonary nodules.